Responding to 103 (Obviousness) Rejections

In the Rejection Resolver, 103 rejections are marked with an orange badge. The card shows all references in the combination, the affected claims, and the examiner motivation for combining the references.

Abigail maps each reference to the claim elements it allegedly teaches. For a 103 rejection, different references cover different elements. The matrix shows which reference teaches which element, and identifies gaps where no reference provides support.

The AI expert analyzes the examiner stated reason for combining references. Common rebuttals include: no teaching or suggestion to combine, references from different fields, and hindsight reconstruction. Abigail generates specific arguments based on the actual references.

Abigail searches the cited references for language that teaches away from the combination. If Reference A warns against or discourages the modification that Reference B suggests, you have a powerful teaching away argument.

If the combination of claimed elements produces results that would be unexpected to one of ordinary skill, Abigail helps you articulate this. The AI identifies which claimed effects differ from what the prior art suggests.

Abigail flags opportunities for secondary considerations: commercial success, long-felt need, failure of others, and skepticism. These objective indicia of non-obviousness can be decisive, especially when the technical arguments are close.

When argument alone may not suffice, Abigail suggests narrowing amendments that add uncovered elements or distinguish the claims from the combination. Each suggested amendment includes a new matter analysis to ensure specification support.

Build your response by selecting arguments and amendments. The Canvas organizes them into a structured 103 response: identification, claim-by-claim analysis, motivation-to-combine rebuttal, and conclusion.